How much visual information about the retinal images can be extracted from the different layers of the visual pathway? This question depends on the complexity of the visual input, the set of transforms applied to this multivariate input, and the noise of the sensors in the considered layer. Separate subsystems (e.g. opponent channels, spatial filters, nonlinearities of the texture sensors) have been suggested to be organized for optimal information transmission. However, the efficiency of these different layers has not been measured when they operate together on colorimetrically calibrated natural images and using multivariate information-theoretic units over the joint spatio-chromatic array of responses. In this work, we present a statistical tool to address this question in an appropriate (multivariate) way. Specifically, we propose an empirical estimate of the information transmitted by the system based on a recent Gaussianization technique. The total correlation measured using the proposed estimator is consistent with predictions based on the analytical Jacobian of a standard spatio-chromatic model of the retina–cortex pathway. If the noise at certain representation is proportional to the dynamic range of the response, and one assumes sensors of equivalent noise level, then transmitted information shows the following trends: (1) progressively deeper representations are better in terms of the amount of captured information, (2) the transmitted information up to the cortical representation follows the probability of natural scenes over the chromatic and achromatic dimensions of the stimulus space, (3) the contribution of spatial transforms to capture visual information is substantially greater than the contribution of chromatic transforms, and (4) nonlinearities of the responses contribute substantially to the transmitted information but less than the linear transforms.

中文翻译：

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通过高斯化可从不同神经层获得时空色信息

可以从视觉通路的不同层提取多少关于视网膜图像的视觉信息？这个问题取决于视觉输入的复杂性，应用于此多元输入的一组变换以及所考虑层中传感器的噪声。已经建议组织单独的子系统（例如，对手通道，空间滤波器，纹理传感器的非线性），以实现最佳的信息传输。但是，当这些不同的层在比色校准的自然图像上一起运行并在响应的联合时空-彩色阵列上使用多元信息理论单元时，尚未测量这些层的效率。在这项工作中，我们提供一种统计工具以适当的方式（多元）解决这个问题。特别，我们提出了基于最新高斯化技术的系统传输的信息的经验估计。使用建议的估计器测得的总相关性与基于视网膜-皮层通路的标准时空色模型的解析雅可比行列式的预测一致。如果某种表示形式的噪声与响应的动态范围成正比，并且假定传感器具有等效的噪声水平，则传输的信息将显示以下趋势：（1）在捕获的信息量方面，越深的表示越好， （2）传输到皮层的信息遵循自然场景在刺激空间的色度和消色差维度上的概率，